Dec 17, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Weak data  Source

[ AnalyticsWeek BYTES]

>> Professional Data Recovery Services and Its Amazing Benefits by thomassujain

>> Technology, People, and Process: 3 Pillars of Construction Digital Transformation by analyticsweekpick

>> March 13, 2017 Health and Biotech analytics news roundup by pstein

Wanna write? Click Here

[ FEATURED COURSE]

Tackle Real Data Challenges

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Learn scalable data management, evaluate big data technologies, and design effective visualizations…. more

[ FEATURED READ]

The Black Swan: The Impact of the Highly Improbable

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A black swan is an event, positive or negative, that is deemed improbable yet causes massive consequences. In this groundbreaking and prophetic book, Taleb shows in a playful way that Black Swan events explain almost eve… more

[ TIPS & TRICKS OF THE WEEK]

Fix the Culture, spread awareness to get awareness
Adoption of analytics tools and capabilities has not yet caught up to industry standards. Talent has always been the bottleneck towards achieving the comparative enterprise adoption. One of the primal reason is lack of understanding and knowledge within the stakeholders. To facilitate wider adoption, data analytics leaders, users, and community members needs to step up to create awareness within the organization. An aware organization goes a long way in helping get quick buy-ins and better funding which ultimately leads to faster adoption. So be the voice that you want to hear from leadership.

[ DATA SCIENCE Q&A]

Q:Compare R and Python
A: R
– Focuses on better, user friendly data analysis, statistics and graphical models
– The closer you are to statistics, data science and research, the more you might prefer R
– Statistical models can be written with only a few lines in R
– The same piece of functionality can be written in several ways in R
– Mainly used for standalone computing or analysis on individual servers
– Large number of packages, for anything!

Python
– Used by programmers that want to delve into data science
– The closer you are working in an engineering environment, the more you might prefer Python
– Coding and debugging is easier mainly because of the nice syntax
– Any piece of functionality is always written the same way in Python
– When data analysis needs to be implemented with web apps
– Good tool to implement algorithms for production use

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek: Big Data at Work: Paul Sonderegger

 @AnalyticsWeek: Big Data at Work: Paul Sonderegger

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

The data fabric is the next middleware. – Todd Papaioannou

[ PODCAST OF THE WEEK]

Solving #FutureOfOrgs with #Detonate mindset (by @steven_goldbach & @geofftuff) #FutureOfData #Podcast

 Solving #FutureOfOrgs with #Detonate mindset (by @steven_goldbach & @geofftuff) #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

571 new websites are created every minute of the day.

Sourced from: Analytics.CLUB #WEB Newsletter

Dec 10, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Accuracy check  Source

[ FEATURED COURSE]

Data Mining

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Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations… more

[ FEATURED READ]

The Black Swan: The Impact of the Highly Improbable

image

A black swan is an event, positive or negative, that is deemed improbable yet causes massive consequences. In this groundbreaking and prophetic book, Taleb shows in a playful way that Black Swan events explain almost eve… more

[ TIPS & TRICKS OF THE WEEK]

Analytics Strategy that is Startup Compliant
With right tools, capturing data is easy but not being able to handle data could lead to chaos. One of the most reliable startup strategy for adopting data analytics is TUM or The Ultimate Metric. This is the metric that matters the most to your startup. Some advantages of TUM: It answers the most important business question, it cleans up your goals, it inspires innovation and helps you understand the entire quantified business.

[ DATA SCIENCE Q&A]

Q:Do you think 50 small decision trees are better than a large one? Why?
A: * Yes!
* More robust model (ensemble of weak learners that come and make a strong learner)
* Better to improve a model by taking many small steps than fewer large steps
* If one tree is erroneous, it can be auto-corrected by the following
* Less prone to overfitting

Source

[ VIDEO OF THE WEEK]

#FutureOfData with @theClaymethod, @TiVo discussing running analytics in media industry

 #FutureOfData with @theClaymethod, @TiVo discussing running analytics in media industry

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Processed data is information. Processed information is knowledge Processed knowledge is Wisdom. – Ankala V. Subbarao

[ PODCAST OF THE WEEK]

Unconference Panel Discussion: #Workforce #Analytics Leadership Panel

 Unconference Panel Discussion: #Workforce #Analytics Leadership Panel

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

For a typical Fortune 1000 company, just a 10% increase in data accessibility will result in more than $65 million additional net income.

Sourced from: Analytics.CLUB #WEB Newsletter

The value of business intelligence for your business

The business environment is currently depicted as a highly competitive one, and to survive, one must come up with ingenious tactics to remain relevant. The current era of big data catches many business owners by surprise, with overwhelming volumes of information. But to remain relevant in their field, these company executives need to develop a way to understand and take full control of such information to derive the best value for their organization. 

For a person looking to make data-driven decisions, other than relying solely on their gut, you might find it useful looking into the possibilities of business intelligence. Business intelligence enables you to get a comprehensive report of all the complicated questions you may have regarding your company operations and successfully tracking KPIs by getting notifications.  

As company executives continue to identify and learn new strategies to implement, it would be prudent to consider current best practices to enable their organization to succeed.  

In this post, you will have a better understanding of what business intelligence is, its architecture, the benefits it has to businesses, and other tips on how to incorporate it successfully into your organization.
 

What is Business Intelligence? 

To identify their full potential, companies across most (if not all) sectors tend to drive towards innovation, effective decision-making, improving quality, and reducing overall costs. While these goals might prove challenging to achieve, they are easily achievable by harnessing the power of analytics.  

Understanding the ‘how,’ ‘why,’ and ‘where’ has greatly influenced the growth of companies, mostly attributed to the explosive dawn of technological advancements and business data. 

To achieve all these, organizations opt for business intelligence. Generally speaking, Business Intelligence (BI) is an information system that translates data into easy-to-understand analytical information.   

A broader definition of BI, according to the Gartner IT glossary, “Business Intelligence is an umbrella term that includes the applications, infrastructure, tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance.” 

According to such explanations, it is clear that the main objective of BI is to identify the avenues that a business can profit from data. However, this goes beyond just collecting data and includes data analysis procedures and other business processes. 

Business intelligence leverages certain services and software to transform data into actionable insights that strategically and tactically impact business decisions. BI tools are used to analyze such data and present it through summaries, reports, charts, graphs, dashboards, maps, etc., to help provide detailed information that can be used to make crucial company decisions.  

Business intelligence vs. business analytics 

Business intelligence is a descriptive aspect, telling you of what’s going on now and what factors that got us to this particular state in the past. How are sales prospects? How many members of the organization have you gained or lost within the last month? This creates the borderline between business intelligence and another closely related term – business analytics. 

While BI is descriptive, showing you what happened in the past to influence what’s happening now, business analytics is predictive. This means BA shows you what will happen in the near future, enabling you to fine-tune your approaches to get better results.  

In a nutshell, business analytics is mainly thought of as a sub-field of the broader field of data analytics, but only focusing on businesses. However, the distinction between business intelligence and business analytics actually, lies beyond the timeframe mentioned. Unlike business analytics, BI aims to deliver candid snapshots of the current state of affairs to managers without being complicated even for the non-technical end-users.

The value of business intelligence 

As the growth of e-commerce continues to saturate the market, the importance of BI becomes even more apparent than ever. Business owners have to make smart decisions regarding how they wish to see their marketing spend, as anything a consumer now wants is only a click away.  

But why would you incorporate BI into your strategies? 

There are many compelling reasons behind this: improving performance, boosting sales, long-term customer relations, etc., ‒ all built through better customer experiences. Integrating business intelligence into your operations helps your company by delivering value through the following ways: 

Effective decision-making 

The sole reason behind the implementation of BI is to convert raw company data into analyzable, well-structured insights that enable the organizational executives to implement strategic decision-making.  

Great business intelligence means having all your business data in a unified dashboard to include all the relevant data from different areas such as finance, sales, and many others – all that aim to provide a holistic view of the business. At the end of it all, business decisions will be made based on facts rather than assumptions. 

Sales & marketing 

Incorporating BI data allows a company to boost a current marketing campaign’s performance, increasing sales in the long run. Through BI, the sales department can get the right tools to help measure consumer trends through improved visibility. You can also get specialized features that help track and measure sales & marketing campaigns, providing the relevant data that would support future marketing initiatives. 

Customer experience 

Business intelligence is also helpful by delivering the necessary information to help companies understand how their customers interact with their business. Data accuracy is improved when one can access all customer information from a single dashboard, enabling the businesses to enhance customer support, engagement, and experience.   

Moreover, BI helps analyze customer insights to improve the targeting and segmentation of the different categories of customers. This helps identify which resources need to be applied for the businesses to attract only valuable customers to achieve particular goals. 

Boosting productivity 

The automation of routine tasks through BI helps an organization to refine its operational processes. While there are many project management software that can do the same, Business intelligence introduces ways to seamlessly improve inventory control and reduce inefficient constrictions within the organizational structure. Easy-to-access centralized data also cuts down on the administration time and efforts, simultaneously boosting data integrity and productivity. 

Data accuracy and compliance 

The centralized nature of BI data boosts transparency in organizational operations. As it offers you a holistic view of your business and customers, BI also seeks to expose the errors that might lead to lots of downtime and wasted resources.  

Companies are now tasked with heavier responsibilities when it comes to the protection of personal data. There is a keen focus on organizations to adhere to data protection regulations should they wish to store customer information. Implementing BI tools ensures that businesses can address the issue of data governance and integrity. 

Tips for choosing the right business intelligence tools 

Going for the right BI tools is as important as collecting the data itself. There are many business intelligence software that one can go for, but how will you pick the one that best fits your organizational needs? 

Here are three important factors to consider: 

Integration  

Before implementing any BI tools, every company has its own reporting processes. Ensure that the tools you go for are easily integrated with existing structures and can easily incorporate the data received from different sources.  

Identify your immediate goals. 

One of the first steps to undertake before getting a business intelligence tool is identifying the goals you wish to achieve. Setting parameters from the initial stages enables you to glean the right data. 

User-friendliness 

Before settling on a tool, ensure that it has an intuitive interface that’s easy to use by all the approved users. Nothing is worse than any system that’s clunky and hard to operate. This means that the BI tool has to be easy to access, operate, and translate the information it provides. 

Bottom Line 

Implementing a good business intelligence structure is now a necessity for any organization that wishes to succeed. Harness the power of BI tools to improve your company’s operations and enjoy all the positive impacts it will have on your business. 

The post The value of business intelligence for your business appeared first on Big Data Made Simple.

Source

“To Cloud or Not”: Practical Considerations for Disaster Recovery and High Availability in Public Clouds

The perceived benefits of low cost storage, per-usage pricing models, and flexible accessibility—and those facilitated by multi-tenant, public cloud providers in particular—are compelling. Across industries and use cases, organizations are hastening to migrate to the cloud for applications that are increasingly becoming more mission critical with each deployment.

What many fail to realize (until after the fact) is that the question of security is not the only cautionary consideration accompanying this change in enterprise architecture. There are also numerous distinctions related to disaster recovery, failover clustering, and high availability for public cloud use cases which drastically differ from conventional on-premise methods for ensuring business continuity. Most times, businesses are tasked with making significant network configurations to enable these preventive measures which can ultimately determine how successful cloud deployments are.

“Once you’ve made the decision that the cloud is your platform, high availability and security are two things you can’t do without,” explained Dave Bermingham, Senior Technical Evangelist, Microsoft Cloud and Datacenter Management MVP at SIOS. “You have them on-prem. Whenever you have a business critical application, you make sure you take all the steps you can to make sure it’s highly available and your network is secure.”

Availability Realities
The realities of the potential for high availability in the cloud vastly differ from their general perception. According to Bermingham, most of the major public cloud providers such as AWS, Google, Azure and others “have multiple data centers across the entire globe. Within each geographic location they have redundancy in what they call zones. Each region is divided so you can have zone one and zone two be entirely dependent of one another, so there should be no single point of failure between the zones.” The standard promises of nearly 100 percent availability contained in most service-level agreements are predicated on organizations running instances in more than one zone.

However, for certain critical applications such as database management systems like Microsoft SQL Server, for example, “the data is being written in one instance,” noted Bermingham. “Even if you have a second instance up and running, it’s not going to do you any good because the data written on the primary instance won’t be on the secondary instance unless you take steps to make that happen.” Some large cloud providers don’t have Storage Area Networks (SANs) used for conventional on-premise high availability, while there are also few out-the-box opportunities for failovers between regions. The latter is especially essential when “you have a larger outage that affects an entire region,” Bermingham said. “A lot of what we’ve seen to date has been some user error…that has a far reaching impact that could bring down an entire region. These are also susceptible to natural disasters that are regional specific.”

Disaster Recovery
Organizations can maximize disaster recovery efforts in public clouds or even mitigate the need for them with a couple different approaches. Foremost of these involves SANless clusters, which provide failover capabilities not predicated on SAN. Instead of relying on storage networks not supported by some large public clouds, this approach relies on software to facilitate failovers via an experience that is “the same as their experience on-prem with their traditional storage cluster,” Bermingham mentioned. Moreover, it is useful for standard editions of database systems like SQL Server as opposed to options like Always On availability groups.

The latter enables the replication of databases and failovers, but is a feature of the pricey enterprise edition of database management systems such as SQL Server. These alternative methods to what public clouds offer for high availability can assist with redundancy between regions, as opposed to just between zones. “You really want to have a plan B for your availability beyond just distributed across different zones in the same region,” Bermingham commented. “Being able to get your data and have a recovery plan for an entirely different region, or even from one cloud provider to another if something really awful happened and Google went offline across multiple zones, that would be really bad.”

Business Continuity
Other factors pertaining to inter-region disaster recovery expressly relate to networking differences between public clouds and on-premise settings. Typically, when failing over to additional clusters clients can simply connect to a virtual IP address that moves between servers depending on which node is active at that given point in time. This process involves gratuitous Address Resolution Protocols (ARPs), which are not supported by some of the major public cloud vendors. One solution for notifying clients of an updated IP address involves “creating some host-specific routes in different subnets so each of your nodes would live in a different subnet,” Bermingham said. “Depending upon whichever node is online, it will bring an IP address specific to that subnet online. Then, the routing tables would automatically be configured to route directly to that address with a host-specific route.”

Another option is to leverage an internal load-bouncer for client re-direction, which doesn’t work across regions. According to Bermingham: “Many people want to not only have multiple instances in different zones in a same region, but also some recovery option should there be failure in an entire region so they can stand up another instance in an entirely different region in Google Cloud. Or, they can do a hybrid cloud and replicate back on-prem, then use your on-prem as a disaster recovery site. For those configurations that span regions, the route update method is going to be the most reliable for client re-direction.”

Security Necessities
By taking these dedicated measures to ensure business continuity, disaster recovery, and high availability courtesy of failovers, organizations can truly make public cloud deployments a viable means of extracting business value. They simply require a degree of upfront preparation which many businesses aren’t aware of until they’ve already invested in public clouds. There’s also the issue of security, which correlates to certain aspects of high availability. “A lot of times when you’re talking about high availability, you’re talking about moving data across networks so you have to leverage the tools the cloud provider gives you,” Bermingham remarked.“That’s really where high availability and security intersect: making sure your data is secure in transit, and the cloud vendors will give you tools to ensure that.”

Source

Dec 03, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Data interpretation  Source

[ AnalyticsWeek BYTES]

>> Bringing technology to the doorsteps of India’s smallholder farmers for climate resilience by analyticsweekpick

>> Why do we use surveys to measure customer loyalty? by bobehayes

>> What does ‘Bandersnatch’ teach us about data storytelling? by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

Statistical Thinking and Data Analysis

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This course is an introduction to statistical data analysis. Topics are chosen from applied probability, sampling, estimation, hypothesis testing, linear regression, analysis of variance, categorical data analysis, and n… more

[ FEATURED READ]

On Intelligence

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Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one strok… more

[ TIPS & TRICKS OF THE WEEK]

Data Analytics Success Starts with Empowerment
Being Data Driven is not as much of a tech challenge as it is an adoption challenge. Adoption has it’s root in cultural DNA of any organization. Great data driven organizations rungs the data driven culture into the corporate DNA. A culture of connection, interactions, sharing and collaboration is what it takes to be data driven. Its about being empowered more than its about being educated.

[ DATA SCIENCE Q&A]

Q:When would you use random forests Vs SVM and why?
A: * In a case of a multi-class classification problem: SVM will require one-against-all method (memory intensive)
* If one needs to know the variable importance (random forests can perform it as well)
* If one needs to get a model fast (SVM is long to tune, need to choose the appropriate kernel and its parameters, for instance sigma and epsilon)
* In a semi-supervised learning context (random forest and dissimilarity measure): SVM can work only in a supervised learning mode

Source

[ VIDEO OF THE WEEK]

@DrewConway on creating socially responsible data science practice #FutureOfData #Podcast

 @DrewConway on creating socially responsible data science practice #FutureOfData #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Getting information off the Internet is like taking a drink from a firehose. – Mitchell Kapor

[ PODCAST OF THE WEEK]

Andrea Gallego(@risenthink) / @BCG on Managing Analytics Practice #FutureOfData #Podcast

 Andrea Gallego(@risenthink) / @BCG on Managing Analytics Practice #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

73% of organizations have already invested or plan to invest in big data by 2016

Sourced from: Analytics.CLUB #WEB Newsletter

Nov 26, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Data security  Source

[ AnalyticsWeek BYTES]

>> What is a Sales Funnel, Examples and How to Create One by administrator

>> How to Avoid Data Leakage When Performing Data Preparation by administrator

>> Introducing MLflow: an Open Source Machine Learning Platform by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

Machine Learning

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6.867 is an introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending … more

[ FEATURED READ]

Introduction to Graph Theory (Dover Books on Mathematics)

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A stimulating excursion into pure mathematics aimed at “the mathematically traumatized,” but great fun for mathematical hobbyists and serious mathematicians as well. Requiring only high school algebra as mathematical bac… more

[ TIPS & TRICKS OF THE WEEK]

Data Analytics Success Starts with Empowerment
Being Data Driven is not as much of a tech challenge as it is an adoption challenge. Adoption has it’s root in cultural DNA of any organization. Great data driven organizations rungs the data driven culture into the corporate DNA. A culture of connection, interactions, sharing and collaboration is what it takes to be data driven. Its about being empowered more than its about being educated.

[ DATA SCIENCE Q&A]

Q:Explain what a long-tailed distribution is and provide three examples of relevant phenomena that have long tails. Why are they important in classification and regression problems?
A: * In long tailed distributions, a high frequency population is followed by a low frequency population, which gradually tails off asymptotically
* Rule of thumb: majority of occurrences (more than half, and when Pareto principles applies, 80%) are accounted for by the first 20% items in the distribution
* The least frequently occurring 80% of items are more important as a proportion of the total population
* Zipf’s law, Pareto distribution, power laws

Examples:
1) Natural language
– Given some corpus of natural language – The frequency of any word is inversely proportional to its rank in the frequency table
– The most frequent word will occur twice as often as the second most frequent, three times as often as the third most frequent…
– The” accounts for 7% of all word occurrences (70000 over 1 million)
– ‘of” accounts for 3.5%, followed by ‘and”…
– Only 135 vocabulary items are needed to account for half the English corpus!

2. Allocation of wealth among individuals: the larger portion of the wealth of any society is controlled by a smaller percentage of the people

3. File size distribution of Internet Traffic

Additional: Hard disk error rates, values of oil reserves in a field (a few large fields, many small ones), sizes of sand particles, sizes of meteorites

Importance in classification and regression problems:
– Skewed distribution
– Which metrics to use? Accuracy paradox (classification), F-score, AUC
– Issue when using models that make assumptions on the linearity (linear regression): need to apply a monotone transformation on the data (logarithm, square root, sigmoid function…)
– Issue when sampling: your data becomes even more unbalanced! Using of stratified sampling of random sampling, SMOTE (‘Synthetic Minority Over-sampling Technique”, NV Chawla) or anomaly detection approach

Source

[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with  John Young, @Epsilonmktg

 #BigData @AnalyticsWeek #FutureOfData #Podcast with John Young, @Epsilonmktg

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[ QUOTE OF THE WEEK]

Data is not information, information is not knowledge, knowledge is not understanding, understanding is not wisdom. – Clifford Stoll

[ PODCAST OF THE WEEK]

Jeff Palmucci @TripAdvisor discusses managing a #MachineLearning #AI Team

 Jeff Palmucci @TripAdvisor discusses managing a #MachineLearning #AI Team

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[ FACT OF THE WEEK]

Decoding the human genome originally took 10 years to process; now it can be achieved in one week.

Sourced from: Analytics.CLUB #WEB Newsletter